Executive Brief

Chitti.AI for Mobility Manufacturing

An edge AI quality intelligence layer for production lines, supplier networks, and service workflows.

Executive Summary

Chitti.AI enables factories to run real-time visual inspection on edge devices, capture traceable evidence, and convert inspection data into quality intelligence. Designed for mobility and manufacturing environments where every second on the line matters, Chitti operates offline-first, deploys on affordable hardware, and integrates with existing factory workflows.

Why Now

EV and mobility manufacturing are becoming more complex — more components, tighter tolerances, higher line speeds.
Supplier networks need better visibility into part quality across distributed manufacturing ecosystems.
Manual inspection does not scale consistently across shifts, operators, and plants.
Factories need faster quality feedback loops — real-time detection instead of end-of-line sampling.
Edge AI is now deployable on affordable hardware — Raspberry Pi, Jetson Nano — making it cost-effective for production lines.

Where Chitti Fits

Production Line Inspection

Real-time defect detection at line speed on edge devices.

Supplier Quality

Incoming QC with traceable records and supplier scorecards.

EV Component Inspection

Battery pack weld, connector, and seal inspection.

Packaging and Label Verification

Automated verification of labels, codes, and packaging.

Warranty/Service Image Triage

Image-based triage for warranty claims and service inspections.

Quality Dashboarding

Real-time metrics, defect trends, and alert dashboards.

Pilot Model

One line. One defect class. One measurable quality metric.

W1-2
Setup & Data
Line selection, data capture, defect taxonomy
W3-4
Model & Deploy
Model adaptation, edge setup, shadow mode
W5-6
Validate & Report
Dashboard validation, pilot report, scale rec

Success Metrics

Defect Detection Consistency
Pilot target
Measured against existing QC process
False Positive Rate
Tracked per shift
Operator override rate measured continuously
Inspection Cycle Time
~200ms target
Per-inference latency on edge hardware
Rework Reduction
Measurable
Opportunity analysis from pilot data
Alert Response Time
Real-time
Supervisor notification within seconds
Traceability
100% audit trail
SHA-256 chain for every inspection
Supplier Quality Score
Per-supplier
Trended across shipments and time
Cost Per Line
Estimated
Based on line complexity and hardware

Scale Path

Single line → multiple lines → supplier network → service/warranty quality intelligence

Single Line
Multiple Lines
Supplier Network
Service/Warranty